Portrait of Dhanya Sridhar

Dhanya Sridhar

Core Academic Member

dhanya.sridhar@mila.quebec

Canada CIFAR AI Chair

Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research

Research Topics

Causality

Deep Learning

Probabilistic Models

Reasoning

Representation Learning

Biography

Dhanya Sridhar is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core academic member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair.

She was a postdoctoral researcher at Columbia University and received her doctorate from the University of California, Santa Cruz.

In brief, Sridhar’s research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently and discover new knowledge alongside us.

Current Students

Philippe Brouillard

PhD - Université de Montréal

Co-supervisor :

Alexandre Drouin

philippebrouillard@gmail.com

Collaborating researcher - Helmholtz AI

Independent visiting researcher - University of Maryland College Park

Research Intern - McGill University

Co-supervisor :

Guillaume Lajoie

Master's Research - Université de Montréal

Principal supervisor :

Guillaume Lajoie

tejaskasetty@gmail.com

Cristian Dragos Manta

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

tom.marty@mila.quebec

Mizu Nishikawa-Toomey

PhD - Université de Montréal

Principal supervisor :

Laurent Charlin

PhD - Université de Montréal

Principal supervisor :

Simon Lacoste-Julien

Publications

Estimating Social Influence from Observational Data

Caterina De Bacco

David Blei

We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The k… (see more)ey challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.

2022-06-28

Proceedings of the First Conference on Causal Learning and Reasoning (published)

Heterogeneous Supervised Topic Models

Hal Daumé III

David Blei

2022-06-17

Transactions of the Association for Computational Linguistics (published)